Classification of Medical Imaging Modalities Based on Visual and Signal Features

  • Amir Rajaei
  • Elham Dallalzadeh
  • Lalitha Rangarajan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)


In this paper, we present an approach to classify medical imaging modalities. Medical images are preprocessed in order to remove noises and enhance their content. The features based on texture, appearance and signal are extracted. The extracted features are concatenated to each other and considered for classification. KNN and SVM classifiers are applied to classify medical imaging modalities. The proposed approach is conducted on IMageCLEF2010 dataset. We achieve classification accuracy 95.39 % that presents the efficiency of our proposed approach.


Medical imaging modalities classification Feature extraction Texture feature Appearance feature Signal feature K-nearest neighbor Support vector machine 



The authors would like to thank TM Lehmann, Department of Medical Informatics, RWTH Aachen, Germany, for making the database available for the experiments.


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Copyright information

© Springer India 2013

Authors and Affiliations

  • Amir Rajaei
    • 1
  • Elham Dallalzadeh
    • 1
  • Lalitha Rangarajan
    • 1
  1. 1.Department of Studies in Computer ScienceManasagangothri, University of MysoreMysoreIndia

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